Flow control also follows this trend of increasing integration of advanced machine learning methods, notably leveraging the paradigm of reinforcement learning (RL), based on the idea of trial-and-error. RL consists in evaluating a given control law (also called a policy) on a target system (referred to as the environment). The evaluation data is then used to tweak the control law to maximise a given performance metric, with the underlying idea of promoting beneficial actions or strategies with respect to the metric. In the case of deep reinforcement learning, the policy is embodied by a neural network structure tasked with providing a control action given observed measurement. Exploring new control strategies plays a decisive role in the performance of such methods. A flourishing literature [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, implements these methods and demonstrates their significant potential for flow control. Applications range from the control of a Rayleigh-Bénard convection cell [7]} to destabilised 1D liquid films [8]} and even to transitional flows, that are of crucial interest for the control of turbulence, for instance with [9]} who successfully implement RL-trained flow control on a cylinder wake.
